Undergraduate Course: Machine Learning & Pattern Recognition (Level 10) (INFR10036)
|School||School of Informatics
||College||College of Science and Engineering
||Availability||Available to all students
|Credit level (Normal year taken)||SCQF Level 10 (Year 4 Undergraduate)
|Home subject area||Informatics
||Other subject area||None
||Taught in Gaelic?||No
|Course description||Both the study of Artificial Intelligence - understanding how to build learning machines - and the business of developing tools to analyse the numerous increasing data sources involves developing a systematic understanding of how we can learn from data. A principled approach to this problem is critical given the wide differences in the places these methods need to be used.
This course is a foundational course for anyone pursuing machine learning, or interested in the intelligent utilisation of machine learning methods. The primary aim of the course is enable the student to think coherently and confidently about machine learning problems, and present the student with a set of practical tools that can be applied to solve real-world problems in machine learning, coupled with an appropriate, principled approach to formulating a solution.
This course avoids the potential pitfalls of simply presenting a set of machine learning tools as if they were an end in themselves, but follows the basic principles of machine learning methods in showing how the different tools are developed, how they are related, how they should be deployed, and how they are used in practice. The course presents a number of methods in machine learning that are increasingly used, including Bayesian methods, and Gaussian processes.
Entry Requirements (not applicable to Visiting Students)
|Prohibited Combinations|| Students MUST NOT also be taking
Machine Learning & Pattern Recognition (Level 10) (INFR10036)
||Other requirements|| Familiarity with basic mathematics, including algebra and calculus is essential. A reasonable knowledge of computational, logical, geometric and set-theoretic concepts is assumed. Working knowledge of vectors and matrices is also necessary. A basic grasp of probability and partial differentiation, is strongly recommended.
|Additional Costs|| None
Information for Visiting Students
|Displayed in Visiting Students Prospectus?||No
Course Delivery Information
|Not being delivered|
Summary of Intended Learning Outcomes
|1-A way of thinking - the course introduces an approach to thinking about machine learning problems, the core issues in approaching any machine learning issue, and in developing your own machine learning solutions. Machine learning is about models more than it is about algorithms. Learning Outcome: The students will be able to describe why a particular model is appropriate in a given situations, formulate the model and use it appropriately.
2-A strong foundation - the course will provide students with the core techniques and methods needed to use machine learning in any area. Learning Outcome: The student will be able to analytically demonstrate how different models and different algorithms are related to one another.
3-Practical capability - the course will provide students with the theoretical background needed to assess good practice, along with the practical experience of trying out machine learning techniques in a MATLAB environment. Learning Outcome: Students will be able to implement a set of practical methods, given example algorithms in MATLAB, and be able to program solutions to some given real world machine learning problems, using the toolbox of practial methods presented in the lectures.
4-Thoroughness - students will leave the course with a deep understanding of machine learning, its aims and limitations, from a viewpoint of modelling. Learning Outcome: Given a particular situation, students will be able be able to justify why a given model is appropriate for the situation or why it is not appropriate. Students will be able to developing an appropriate algorithm from a given model, and demonstrate the use of that method.
5-Coherence - the course provide a unifying coherent view on machine learning. Learning Outcome: students will be able to design and compare machine learning methods, and discuss how different methods relate to one another and will be able to develop new and appropriate machine learning methods appropriate for particular problems.
|Written Examination 80|
Assessed Assignments 20
Oral Presentations 0
There will be two assignments for the course, one for each half of the course contents. This will involve practical hands on data analysis as well as questions about the ideas on the course.
If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year.
||* Data and Models: Introducing Data, Probability and Bayesian Presumptions.
* Simple Distributions, Maximum Likelihood and Bayesian Estimation.
* Bayesian Sets Example
* The Exponential Family
* Multivariate Gaussians, PCA and PPCA. Bayesian Gaussian
* Linear Parameter Models, Bayesian Regression
* Logistic Regression and Neural Networks
* Approximate Methods: Laplace, Variational Methods, Sampling.
* Na´ve Bayes, Class Conditional Gaussians, Gaussian Mixtures and EM.
* Gaussian Processes and Kernel Methods
* Bayesian Decision Theory.
Relevant QAA Computing Curriculum Sections: Artificial Intelligence, Human-Computer Interaction (HCI), Intelligent Information Systems Technologies, Natural Language Computing, Simulation and Modelling, Theoretical Computing.
||Machine Learning: a Probabilistic Perspective. Kevin Patrick Murphy. MIT press. 2012
Timetabled Laboratories 0
Non-timetabled assessed assignments 22
Private Study/Other 50
|Course organiser||Dr Amos Storkey
Tel: (0131 6)51 1208
|Course secretary||Miss Kate Weston
Tel: (0131 6)50 2692